Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.
Attempting to start a local H2O server...
  Java Version: openjdk version "11.0.11" 2021-04-20; OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2.18.04); OpenJDK 64-Bit Server VM (build 11.0.11+9-Ubuntu-0ubuntu2.18.04, mixed mode, sharing)
  Starting server from /usr/local/lib/python3.7/dist-packages/h2o/backend/bin/h2o.jar
  Ice root: /tmp/tmpx7kye1gn
  JVM stdout: /tmp/tmpx7kye1gn/h2o_unknownUser_started_from_python.out
  JVM stderr: /tmp/tmpx7kye1gn/h2o_unknownUser_started_from_python.err
  Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321 ... successful.

H2O_cluster_uptime:	03 secs
H2O_cluster_timezone:	Etc/UTC
H2O_data_parsing_timezone:	UTC
H2O_cluster_version:	3.32.1.4
H2O_cluster_version_age:	11 days
H2O_cluster_name:	H2O_from_python_unknownUser_0tvhwr
H2O_cluster_total_nodes:	1
H2O_cluster_free_memory:	3.172 Gb
H2O_cluster_total_cores:	2
H2O_cluster_allowed_cores:	2
H2O_cluster_status:	accepting new members, healthy
H2O_connection_url:	http://127.0.0.1:54321
H2O_connection_proxy:	{"http": null, "https": null}
H2O_internal_security:	False
H2O_API_Extensions:	Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4
Python_version:	3.7.11 final

Parse progress: |█████████████████████████████████████████████████████████| 100%
AutoML progress: |████████████████████████████████████████████████████████| 100%

Leaderboard

Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.

model_id	auc	logloss	aucpr	mean_per_class_error	rmse	mse	training_time_ms	predict_time_per_row_ms	algo
GBM_grid__1_AutoML_20210720_110510_model_1	0.79592	0.254477	0.219521	0.273997	0.271719	0.073831	341	0.049602	GBM
GBM_grid__1_AutoML_20210720_110510_model_2	0.786609	0.288506	0.183858	0.297528	0.287153	0.0824567	288	0.04541	GBM
XGBoost_grid__1_AutoML_20210720_110510_model_6	0.78225	0.250349	0.211155	0.249281	0.266756	0.071159	124	0.023677	XGBoost
XGBoost_grid__1_AutoML_20210720_110510_model_2	0.781446	0.258267	0.230984	0.282165	0.271174	0.0735355	170	0.029304	XGBoost
XGBoost_grid__1_AutoML_20210720_110510_model_5	0.771542	0.256891	0.197349	0.269807	0.270226	0.0730219	151	0.015605	XGBoost
StackedEnsemble_BestOfFamily_AutoML_20210720_110510	0.76968	0.256505	0.208264	0.328593	0.270608	0.0732289	394	0.093196	StackedEnsemble
XGBoost_grid__1_AutoML_20210720_110510_model_1	0.76858	0.328	0.193323	0.295708	0.295434	0.0872812	151	0.019162	XGBoost
GBM_1_AutoML_20210720_110510	0.767564	0.274064	0.184669	0.290545	0.278576	0.0776043	300	0.01698	GBM
XGBoost_grid__1_AutoML_20210720_110510_model_4	0.766887	0.310873	0.1851	0.309675	0.289033	0.0835403	150	0.033728	XGBoost
XGBoost_3_AutoML_20210720_110510	0.766548	0.271105	0.183988	0.286355	0.280231	0.0785293	141	0.019065	XGBoost
XGBoost_grid__1_AutoML_20210720_110510_model_8	0.765913	0.259283	0.172262	0.341163	0.269354	0.0725518	103	0.012563	XGBoost
GBM_grid__1_AutoML_20210720_110510_model_4	0.757872	0.258901	0.177716	0.245725	0.271576	0.0737535	200	0.018967	GBM
GBM_5_AutoML_20210720_110510	0.757237	0.254046	0.182285	0.269807	0.267041	0.0713109	155	0.017601	GBM
GBM_grid__1_AutoML_20210720_110510_model_3	0.755502	0.255088	0.184569	0.26422	0.266686	0.0711213	224	0.017665	GBM
XGBoost_grid__1_AutoML_20210720_110510_model_7	0.748773	0.307267	0.178202	0.269807	0.291993	0.0852597	153	0.015149	XGBoost
XGBoost_grid__1_AutoML_20210720_110510_model_9	0.748138	0.271588	0.174836	0.311283	0.272789	0.0744139	86	0.013119	XGBoost
StackedEnsemble_AllModels_AutoML_20210720_110510	0.746657	0.267751	0.161574	0.320637	0.27363	0.0748732	365	0.10245	StackedEnsemble
GBM_2_AutoML_20210720_110510	0.737176	0.262356	0.197863	0.28754	0.269736	0.0727577	197	0.016535	GBM
GBM_3_AutoML_20210720_110510	0.732013	0.26938	0.165151	0.286567	0.275174	0.0757208	275	0.016725	GBM
XGBoost_grid__1_AutoML_20210720_110510_model_3	0.732013	0.26124	0.179714	0.277002	0.269178	0.0724568	102	0.015415	XGBoost

Confusion Matrix

Confusion matrix shows a predicted class vs an actual class.

GBM_grid__1_AutoML_20210720_110510_model_1

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2557828965438706:

		false	true	Error	Rate
0	false	351.0	7.0	0.0196	(7.0/358.0)
1	true	8.0	25.0	0.2424	(8.0/33.0)
2	Total	359.0	32.0	0.0384	(15.0/391.0)

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

Variable Importance Heatmap

Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.

Model Correlation

This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Confusion Matrix

Confusion matrix shows a predicted class vs an actual class.

GBM_grid__1_AutoML_20210720_110510_model_1

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2557828965438706:

		false	true	Error	Rate
0	false	351.0	7.0	0.0196	(7.0/358.0)
1	true	8.0	25.0	0.2424	(8.0/33.0)
2	Total	359.0	32.0	0.0384	(15.0/391.0)

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Checking whether there is an H2O instance running at http://localhost:54321 . connected.

H2O_cluster_uptime:	3 mins 16 secs
H2O_cluster_timezone:	Etc/UTC
H2O_data_parsing_timezone:	UTC
H2O_cluster_version:	3.32.1.4
H2O_cluster_version_age:	11 days
H2O_cluster_name:	H2O_from_python_unknownUser_0tvhwr
H2O_cluster_total_nodes:	1
H2O_cluster_free_memory:	3.163 Gb
H2O_cluster_total_cores:	2
H2O_cluster_allowed_cores:	2
H2O_cluster_status:	locked, healthy
H2O_connection_url:	http://localhost:54321
H2O_connection_proxy:	{"http": null, "https": null}
H2O_internal_security:	False
H2O_API_Extensions:	Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4
Python_version:	3.7.11 final

Parse progress: |█████████████████████████████████████████████████████████| 100%
AutoML progress: |████████████████████████████████████████████████████████| 100%

Leaderboard

Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.

model_id	mean_residual_deviance	rmse	mse	mae	rmsle	training_time_ms	predict_time_per_row_ms	algo
StackedEnsemble_AllModels_AutoML_20210720_110822	17633.2	132.79	17633.2	88.4714	0.163249	271	0.147336	StackedEnsemble
StackedEnsemble_BestOfFamily_AutoML_20210720_110822	17933.6	133.916	17933.6	90.434	0.164036	144	0.093224	StackedEnsemble
GBM_grid__1_AutoML_20210720_110822_model_1	26298.8	162.169	26298.8	112.076	0.175313	424	0.061966	GBM
XGBoost_grid__1_AutoML_20210720_110822_model_4	26312.2	162.21	26312.2	114.551	0.153739	180	0.020614	XGBoost
XGBoost_grid__1_AutoML_20210720_110822_model_3	26845.6	163.846	26845.6	116.373	0.144884	236	0.006608	XGBoost
XGBoost_grid__1_AutoML_20210720_110822_model_5	33015.2	181.701	33015.2	129.204	0.125457	677	0.016865	XGBoost
XGBoost_grid__1_AutoML_20210720_110822_model_9	36505.7	191.065	36505.7	132.228	0.156883	177	0.003776	XGBoost
XGBoost_grid__1_AutoML_20210720_110822_model_2	40280.3	200.7	40280.3	144.697	0.155139	663	0.014117	XGBoost
XGBoost_3_AutoML_20210720_110822	40660.6	201.645	40660.6	145.186	0.17298	166	0.011042	XGBoost
XGBoost_1_AutoML_20210720_110822	40680	201.693	40680	143.499	0.150462	830	0.018056	XGBoost
XGBoost_grid__1_AutoML_20210720_110822_model_1	45457.6	213.208	45457.6	148.708	0.18636	479	0.010564	XGBoost
XGBoost_grid__1_AutoML_20210720_110822_model_8	46342.1	215.272	46342.1	158.695	0.168022	595	0.019797	XGBoost
XGBoost_grid__1_AutoML_20210720_110822_model_6	47555.9	218.073	47555.9	156.613	0.182885	154	0.010723	XGBoost
GBM_1_AutoML_20210720_110822	49826.3	223.218	49826.3	161.259	0.185001	280	0.021325	GBM
XGBoost_2_AutoML_20210720_110822	50881.3	225.569	50881.3	161.075	0.19397	714	0.01349	XGBoost
GBM_3_AutoML_20210720_110822	53412.8	231.112	53412.8	157.073	0.203502	260	0.012073	GBM
GBM_grid__1_AutoML_20210720_110822_model_2	61496.6	247.985	61496.6	178.095	0.190502	461	0.035649	GBM
DRF_1_AutoML_20210720_110822	73276.5	270.696	73276.5	182.53	0.204566	303	0.021696	DRF
GBM_4_AutoML_20210720_110822	75087	274.02	75087	188.544	0.222515	265	0.022493	GBM
XGBoost_grid__1_AutoML_20210720_110822_model_7	80016.5	282.872	80016.5	193.155	0.209027	178	0.022893	XGBoost

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

Variable Importance Heatmap

Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.

Model Correlation

This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

Checking whether there is an H2O instance running at http://localhost:54321 . connected.

H2O_cluster_uptime:	1 min 53 secs
H2O_cluster_timezone:	Etc/UTC
H2O_data_parsing_timezone:	UTC
H2O_cluster_version:	3.32.1.5
H2O_cluster_version_age:	2 days
H2O_cluster_name:	H2O_from_python_unknownUser_hndmmi
H2O_cluster_total_nodes:	1
H2O_cluster_free_memory:	3.172 Gb
H2O_cluster_total_cores:	2
H2O_cluster_allowed_cores:	2
H2O_cluster_status:	locked, healthy
H2O_connection_url:	http://localhost:54321
H2O_connection_proxy:	{"http": null, "https": null}
H2O_internal_security:	False
H2O_API_Extensions:	Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4
Python_version:	3.7.11 final

Parse progress: |█████████████████████████████████████████████████████████| 100%
AutoML progress: |████████████████████████████████████████████████████████| 100%

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.